A lot has been made about the importance of Bitcoin mining over the years, with many people trying to use the cost of production to set a price floor in a similar fashion to the gold market. A simple glance at the mining revenue chart from Blockchain reveals that mining is indeed significant for such estimates.

It is obvious on examination that all structural gaps are closed by mining revenue, even when this does not happen on the price chart. In addition, these bottoms in revenue occur exactly when the price of Bitcoin bottoms.

Mining revenue (candles) and Bitcoin price (orange area plot). The mining revenue always closes the structural gap to the previous revenue high (green horizontal line). This indicates that the Bitcoin market has reached a price bottom.

It is also possible to use this chart on intercycle swings as those structural gaps are also closed! This has been the case since day 1.

The data is available from Blockchain, the chart from Quandl. At tradingview enter QUANDL:BCHAIN/MIREV to call it up.

It’s been a long-time interest of mine to estimate the electricity consumption of Bitcoin’s Proof of Work algorithm. As you will no doubt know, Bitcoin transactions are verified by for-profit miners in a computationally expensive process, so the task basically boils down to knowing the total number of computations per unit time (hash rate) of the network, how many mining machines are operational globally and what their particularly specs are.

My previous attempts were limited to back of the envelope minimum consumption estimates based on assuming everyone was running the latest, most efficient machine. Obviously these are both very unreliable…

Two Bitcoin price predictions (blue and red lines) generated using Facebook’s Prophet package. The actual price data is in green, while the shaded areas denote the respective uncertainty in the estimate. As you can the uncertainty increases into the future. This is particularly the case with the tighter fitting price model (red).

This is a quick look at Facebook’s Prophet machine learning package using the example of Bitcoin. It assumes basic Python knowledge and some familiarity with pandas.

Prophet is Facebook’s open source forecasting procedure for time series data. The idea is that it should make fully automatic forecasting easy even with messy data and it’s currently available in R and Python. I shall be using Python 2.7 in this post.

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not…

What is value and how do we put a price on a thing? Is there some intrinsic factor that we can point to to justify price or is it merely an emergent property of supply and demand?

If we acknowledge the reality of trends-within-trends, then it is not difficult to conceive that dramatic short-term price movements can easily mask slower, yet more profound fundamental cycles. In traditional commodities markets, these so-called fundamentals may include production metrics like rate, volume and cost, as well as market metrics such as trade volume; in other words supply and demand, yes? Well, in an…

Celebrating 50 Years of the Tech Bubble

Did you know that coastline of Great Britain is infinite in length? That’s right, well at least it is to some people. The point is coastlines, like many things in nature such as, oh I don’t know, financial markets say, are fractal. That means they can be subdivided into increasingly smaller, but similarly-shaped parts. Mathematically at least, if you can split a line into infinitely small parts, then its total length is also infinite… in truth it is unknown whether real space can be divided like this…. but the point is… fractals dammit!

How do you know that nasty looking…

Ever asked yourself what happens if you graph the spot price of Bitcoin divided by the cumulative number of altcoins? Yeah, I know. Who would do that….

Well, I was wondering if there was maybe a relationship between the rate that altcoins are launched and Bitcoin’s price action and that maybe it wasn’t as trivial as it sounded.

But I’m not sure what I found, so see for yourself.

Bitcoin spot price divided by the cumulative number of altcoins. The data was taken from coinmarketcap.com, which at the time of writing list 2113 altcoins.

The process was straightforward: for the…

UPDATE: The addresses featured in this post were all liquidated within 24 hours of each other on the 3rd and 4th of December 2018. The beauty of Bitcoin is that transactions are visible, so anyone can trace the movement of this coin. The owner has spread them over numerous addresses each containing 8000 BTC, he/she/it used segwit mixing to do it. Bitupper.com is a block explorer that allows you to explore segwit transactions.

Take a look at The Top 100 Richest Bitcoin addresses and you’ll notice that the Top 5 belong to the exchanges Binance, Bitfinex, Bittrex, Huobi and Bitstamp. These are their cold (offline)wallets and together account for 3.24% of all Bitcoin.

The rest are unspecified. Most of the addresses listed aren’t more than two years old, yet interestingly of the first 10 non-exchange addresses 8 were created between 2011 and 2016. Compared to the rest of the list, this is a relatively high proportion of older addresses. In addition, 7 of them have never made a pay out. …

Bitfury’s new 14nm ASIC Clarke which claims an efficiency of around 18 GH/J.

After a distinct lull in innovation within the crypto mining sector since 2016, this year has seen a flurry of new developments largely driven by new chip technologies. In addition several new manufacturers have entered the industry. How will this effect the network and where is the trend going?

In the first half of the year Japanese tech company GMO Internet announced their entry into Bitcoin mining as well as the retail launch of their B-series miners. These devices, now sold out, feature 7nm ASIC chips making them potentially much more efficient than the previous 16nm state of the art.

**The Science Bit**

While many indicators exist to help technical analysts plan investment strategies, I like to keep my trading toolbox as tidy as possible. RSI, stoch, MACD and even Ichimoku clouds all get a look in, but by far and a way my favourite is the simple moving average (MA). These often get overlooked or derided as lagging indicators but, used correctly, they can help you map out dynamic support and resistances or the flow of your asset, if you like.

MAs are periodic. That means for a given period, say 50, the average is calculated over the preceding 49 candles plus…

As soon as I learned about Google Trends I was instantly fascinated. You could peer into the collective consciousness of the human herd in both time and space, searching keywords by individual country or the world as a whole and over custom date ranges.

But like most things we encounter on the interwebs, it didn’t manage to achieve anything more than curiosity status with me, that is, until I actually found a use for it. …


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